Robust Log-Based Anomaly Detection with Hierarchical Contrastive Learning

Abstract

Logs are widely employed in modern systems to record critical information and serve as an important source for anomaly detection, which has attracted increasing research interests. However, logs usually suffer from perturbations and it makes the existing log-based anomaly detection methods unstable. In this paper, we aim to solve this problem from the perspective of contrastive learning, by which the intrinsic and robust representations of logs are learned for anomaly detection. We propose two data augmentation methods to generate different views at different granularity for log data and design a deep hierarchical contrastive model for anomaly detection. In the contrastive semantic embedding module, we fine-tune a language model with a message-level contrastive loss. And in the contrastive anomaly detection module, we apply a sequence-level contrastive constraint to assist the detection model to learn robust embeddings for log sequences. Experiments on three datasets verify the effectiveness of our proposed method.